论文标题
使用机器学习来预测未来的收入
Using Machine Learning to Forecast Future Earnings
论文作者
论文摘要
在本文中，我们全面评估了在公司基本面的预测（即收益）上采用机器学习模型的可行性和适用性，在此，我们方法的预测结果已与两位分析师的共识估计和传统统计模型进行了彻底比较。结果，我们的模型已经被证明能够作为分析师对公司基本面进行更好预测的有利辅助工具。与以前的传统统计模型（如逻辑回归）中广泛采用相比，我们的方法已经在预测准确性和速度方面取得了令人满意的进步。同时，我们也充满信心，这种模型仍然具有巨大的潜力，我们确实希望在不久的将来，与专业分析师相比，机器学习模型可以产生更好的性能。
In this essay, we have comprehensively evaluated the feasibility and suitability of adopting the Machine Learning Models on the forecast of corporation fundamentals (i.e. the earnings), where the prediction results of our method have been thoroughly compared with both analysts' consensus estimation and traditional statistical models. As a result, our model has already been proved to be capable of serving as a favorable auxiliary tool for analysts to conduct better predictions on company fundamentals. Compared with previous traditional statistical models being widely adopted in the industry like Logistic Regression, our method has already achieved satisfactory advancement on both the prediction accuracy and speed. Meanwhile, we are also confident enough that there are still vast potentialities for this model to evolve, where we do hope that in the near future, the machine learning model could generate even better performances compared with professional analysts.
